This invention relates to the analysis of electrocardiograms (ECGs). More specifically, the invention relates to methods and apparatus for analyzing ECGs.
In hospitals or other health-care settings, it is frequently necessary to observe critical physiological conditions of a patient, including cardiovascular conditions. Such cardiovascular condition data includes ECGs acquired using electrodes applied to the patient.
A typical ECG management system comprises a database of ECGs plus applications software. The ECG management system receives ECG data from a multiplicity of instruments via a plurality of networks, analyzes that ECG data using various programs such as a serial comparison program, generates reports containing the results of the analysis, and then routes those reports to various systems and peripheral devices.
The accuracy of any ECG analysis expert software tool is directly dependent upon the quality of the signal it acquires. In 1979, Marquette Medical Systems introduced an electrocardiograph that simultaneously acquired all of the leads from the 12-lead electrocardiogram. Prior to this time, all commercially available electrocardiographs could only acquire 3 leads at a time. Simultaneous recording was adopted so that the computer could use all signals from all 12 leads to properly detect and classify each QRS complex. The program also applied digital filters which removed power line noise and baseline sway. Typically, ECG data is acquired from the 12 leads for a period of 10 seconds.
All ECG analysis computer programs are composed of two parts: one which measures the waveforms, the other which does the interpretation based on these measurements. The main task of the measurement section is to find the location of the major reference points (that is, the onsets and offsets of the P, QRS and T complexes). After the onsets and offsets of the P, QRS, and T complexes have been demarcated, the waves within each complex are measured according to published standards. These amplitudes and durations result in a measurement matrix containing more than 1000 values. This is then passed to the criteria portion of the ECG analysis program so that it can generate an interpretation, including diagnostic statements referenced via a statement library.
Recent progress in noninvasive electrocardiology has introduced more interesting features for use in diagnosis, including T wave alternans (TWA), QT dispersion (QTD), QT dynamicity (QTDN), short-time heart rate variability (HRV), etc.
T Wave Alternans Alternans is a subtle beat-to-beat change in the repeating pattern of an ECG that can be indicative of electrical instability of the heart and increased susceptibility to sudden cardiac death. Alternans results in an ABABAB . . . pattern of variation of the waveform shape between successive beats in an ECG waveform, and the level of cardiac stability is taken as a characterization of an individual's cardiac electrical stability. In humans, it has been found that the T wave is the best interval of the ECG complex for detecting alternans. That is, a level of variation in the T waves of alternating beats is the best indicator of a patient's level of alternans.
QT Dispersion The calculation of QT interval dispersion from the standard 12-lead ECG is a promising noninvasive measurement of inhomogeneity in myocardial repolarization. The QT dispersion measurement is useful for different clinical applications, namely for assessment of patients at risk for lethal ventricular arrhythmias and for differentiating patients with acute myocardial infarction (AMI) from those with noncardiac chest pain. To compute QT dispersion, one needs to measure QRS complex onset and T wave offset to calculate the QT interval for each lead. A computerized automatic method for calculating QT dispersion was presented by Xue and Reddy in “Algorithms for Computerized QT Analysis”, Journal of Electrocardiology, Vol. 30 Supplement, pp. 198–203 (1998).
QT Dynamicitylt is known that prolongation of the QT interval may be a marker for sudden death. QT dynamicity is used to examine the beat-to-beat change of the QT interval due to changes in heart rate or T morphology.
Heart Rate Variability The autonomic nervous system is the part of the vertebrate nervous system that regulates involuntary action, such as the heart. A significant relationship exists between the autonomic nervous system and cardiovascular mortality, including sudden cardiac death (SCD). Heart rate variability (HRV) is a recognized quantitative marker of autonomic activity. Many commercial devices provide automated measurement of HRV. The term “heart rate variability” has become the conventionally accepted term to describe variations of both instantaneous heart rate and RR intervals (i.e., the interval between consecutive heart beats). Standards for the measurement of HRV are set forth in “Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use”, by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Circulation, Vol. 93, No. 5, Mar. 1, 1996, pp. 1043–1065.
Late Potentials Late potential is the small amplitude signal detected at the end of the QRS complex, so-called “late” potential. Since late potential has very low amplitude (usually lower than 50 μV), it is detected by using high-resolution ECG through a signal averaging technique. In this case “high resolution” means higher amplifier gain (around 1 μV/LSB vs. 5 μV/LSB (least significant bit)) and higher sampling frequency (1000 samples per second or more vs. 125–500 samples per second for conventional ECG). It has been proposed to combine T-wave alternans and late potentials for identifying high-risk patients with impaired left ventricular function. See Kondo et al., “Clinical usefulness of the combination of T wave alternans and late potentials for identifying high-risk patients with moderately or severely impaired left ventricular functions”, Circulation, Vol. 65, No. 7, pp. 649–553 (2001). The late potentials were determined on signal-averaged ECGs.
Intra-QRS Analysis Intra-QRS analysis examines small notches inside the QRS complex based on signal-averaged high-resolution ECGs. It has been shown that the significant notches found inside the QRS complex are correlated with ischemic events before and after PTCA. See Endt et al., “Identification of post-myocardial infarction patients with ventricular tachycardia by time-domain intra-QRS analysis of signal-averaged electrocardiogram and magnetocardiogram”, Med. Biol. Eng. Comput., Vol. 38, No. 6, pp. 659–665 (2000) Many other expert software tools are being used in cardiology. For example, high-resolution ECGs, also referred to as signal-averaged ECGs (SAECGs), have been widely used in cardiology clinical practice. Late potentials detection of SAECGs has been proven useful for predicting lethal ventricular tachyarrhythmia and ventricular fibrillation (VT/VF), and P-wave SAECGs have been used for predicting atrial fibrillation (AF). During the process of high-resolution detection, 200–400 heart beats signals are acquired with higher sampling frequency (1000 Hz vs. 500 Hz for standard 12-lead ECG), and higher resolution (about 1 μV least significant bit vs. 5 μV for standard 12-lead ECG). In current cardiographs, the end results of high-resolution detection are averaged ECGs from three orthogonal leads: X, Y, Z, and some measurements like P wave and QRS duration from the averaged leads. In practice, also 12-lead electrodes are put on a patient's body surface.
With the 200–400 heart beats (5 to 10 minutes) of high-resolution signals from X, Y, Z leads plus standard 12-lead ECGs, a cardiologist has much more information than the data acquired by regular 10-sec 12-lead ECGs. There is a need for methods of extracting more useful features from this increased volume of acquired data for more accurate and more efficient diagnosis of abnormal activity of the heart.